Abstract 852: Predicting progression in metastatic castrate-sensitive prostate cancer by coupling PSA and testosterone dynamics to a mechanistic mathematical model
Authors: Jill Gallaher, Maximilian Strobl, Jeffrey West, Mark Robertson-Tessi, Robert Gatenby, Jingsong Zhang, Alexander R. A. Anderson
Journal: Cancer Research (2023)

Abstract The purpose of this study is to use the dynamics of commonly collected biomarker levels for prostate cancer to identify the underlying cancer cell subtype composition and help predict response to treatment. In castrate-sensitive prostate cancer, the current standard of care is continuous maximum-tolerated dose (MTD) of anti-androgen compounds, which successfully reduces the tumor burden. However, continuous use often leads to treatment resistance. Alternatively, adaptive therapy approaches have been used clinically to delay or prevent resistant outgrowth. In adaptive therapy, a drug is given to maintain a tumor size by killing enough treatment-sensitive cells to shrink the burden but keep enough sensitive cells to suppress the outgrowth of resistant cells. Clinical trials using adaptive therapy approaches have shown longer tumor control compared to MTD for castrate-resistant disease and, more recently, shown feasibility for control in castrate-sensitive disease. This approach relies on using prostate-specific antigen (PSA) as a systemic biomarker as a surrogate for total tumor burden. Because PSA is a product of testosterone metabolism by androgen receptors (AR) in cells, and both testosterone and AR can vary over time and amongst cells, we can use a mathematical model to couple PSA, testosterone, and AR-dependency to dissociate systemic biomarkers from tumor burden and uncover potential mechanisms of resistance. We build a system of ordinary differential equations to investigate the emergence of different resistance mechanisms for metastatic castrate-sensitive prostate cancer patients. We first fit the equations to testosterone dynamics alone. Then, fixing those parameters, we fit to the dynamics of PSA assuming cell subtypes consisting of testosterone-dependent cells, testosterone producers, and AR-overexpressing cells. From the best fits, we can estimate the tumor cell subtype compositions for each patient and test different treatment strategies. We find that patients with tightly correlated testosterone and PSA levels tend to have mostly testosterone-dependent cells and continued robust responses to each round of treatment. Less correlated testosterone and PSA dynamics was usually associated with changes in PSA nadirs, peaks, or rates of return after stopping treatment over subsequent rounds of adaptive therapy. By fitting to both PSA and testosterone dynamics, we can distinguish between tumors that maintain testosterone-dependent cells from those with increasing resistant cell types. This allows for better quantification of the underlying tumor composition to help optimize therapies that target those cell types and better maintain tumor control. Citation Format: Jill Gallaher, Maximilian Strobl, Jeffrey West, Mark Robertson-Tessi, Robert Gatenby, Jingsong Zhang, Alexander R. A. Anderson. Predicting progression in metastatic castrate-sensitive prostate cancer by coupling PSA and testosterone dynamics to a mechanistic mathematical model [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 852.